To do so, we start similarly as RANSAC by first drawing a random sample \(\mathcal {S}\)  of data points.
This is done by a state-of-the-art sampler, , PROSAC [1]} or P-NAPSAC [2]}.
Model instance \(h\)  is estimated from sample \(\mathcal {S}\) .
In order to decide about \(h\)  being dominant or not, we define model quality function \(Q: \mathbb {R}^d \times \mathcal {P}^* \times \mathbb {R} \rightarrow \mathbb {R}\)  similarly as [3]} to be calculated from the inliers of \(h\)  not shared with other instances in \(\mathcal {I}\) , where \(\mathcal {P}^*\)  is the power set of \(\mathcal {P}\) . Considering the RANSAC-like inlier counting, the implied quality is
\(Q_{\text{RSC}}(h, \mathcal {P}, \epsilon ) = \sum _{\mathbf {p}\in \mathcal {P}} [\phi (h, \mathbf {p}) < \epsilon \wedge \phi (\mathcal {I}, \mathbf {p}) \ge \epsilon ],\) 
